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+
         
         
         
         
 

The resulting image is… not very useful. Graph visualizations of this kind @@ -1454,7 +1435,7 @@

Network Communities -../../_images/bf1cc52f9223bcf00140955cc47a00acf78302fc01f9c4a56847aa57e10dab1d.png +../../_images/fe566226de77dc080aa703150aa07ae03800ddcc58409e75f2ea3a77fdc82b65.png + @@ -942,7 +923,8 @@

References @@ -792,7 +773,8 @@

References

- - + +
diff --git a/searchindex.js b/searchindex.js index fd3dd8ef..f8e4289c 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"1. Eulerian Circuit Implementation": [[2, "eulerian-circuit-implementation"]], "1. Your notebook should be a .md file.": [[7, "your-notebook-should-be-a-md-file"]], "2. Eulerian Path Implementation": [[2, "eulerian-path-implementation"]], "2. Use code-generated images as much as possible.": [[7, "use-code-generated-images-as-much-as-possible"]], "3. Add requirements to requirements.txt": [[7, "add-requirements-to-requirements-txt"]], "3. Examples": [[2, "examples"]], "4. User input is not supported yet.": [[7, "user-input-is-not-supported-yet"]], "5. Do not forget to add path of your notebook to index.md.": [[7, "do-not-forget-to-add-path-of-your-notebook-to-index-md"]], "6. Header Levels": [[7, "header-levels"]], "7. You do not need to implement the algorithm in the same exact way as done inside NetworkX.": [[7, "you-do-not-need-to-implement-the-algorithm-in-the-same-exact-way-as-done-inside-networkx"]], "8. Feel free to use real-world datasets": [[7, "feel-free-to-use-real-world-datasets"]], "9. What if the tests are still failing?": [[7, "what-if-the-tests-are-still-failing"]], "A Brief Roadmap for First-time Contributors": [[7, "a-brief-roadmap-for-first-time-contributors"]], "A Motivating Example": [[9, "a-motivating-example"]], "About": [[12, "about"]], "Addendum: The graph may have changed during the iteration.": [[1, "addendum-the-graph-may-have-changed-during-the-iteration"]], "Addendum: Topological sort works on multigraphs as well.": [[1, "addendum-topological-sort-works-on-multigraphs-as-well"]], "Advanced Algorithms": [[5, "advanced-algorithms"]], "Algorithm": [[3, "algorithm"]], "Algorithms": [[4, null]], "Analysis": [[8, "analysis"]], "Applications": [[1, "applications"], [1, "id3"], [3, "applications"], [5, "applications"]], "Assortativity": [[0, "assortativity"], [8, "assortativity"]], "Assortativity Example": [[0, "assortativity-example"]], "Assortativity coefficients": [[0, "assortativity-coefficients"]], "Attribute Assortativity Coefficient": [[0, "attribute-assortativity-coefficient"]], "Augmenting Path & Flow": [[3, "augmenting-path-flow"]], "Basic topological attributes": [[8, "basic-topological-attributes"]], "Betweenness Centrality": [[8, "betweenness-centrality"]], "Bridges": [[8, "bridges"]], "Centrality measures": [[8, "centrality-measures"]], "Closeness Centrality": [[8, "closeness-centrality"]], "Clustering Effects": [[8, "clustering-effects"]], "Combine all steps.": [[1, "combine-all-steps"]], "Contents": [[12, "contents"]], "Contributors Guide": [[7, null]], "Definition": [[1, "definition"], [1, "id1"], [1, "id5"]], "Definitions": [[6, "definitions"]], "Degree Assortativity Coefficient": [[0, "degree-assortativity-coefficient"]], "Degree Centrality": [[8, "degree-centrality"]], "Dinitz\u2019s Algorithm and Applications": [[3, null]], "Dinitz\u2019s algorithm": [[3, "dinitz-s-algorithm"]], "Directed Acyclic Graph": [[1, "directed-acyclic-graph"]], "Directed Acyclic Graphs & Topological Sort": [[1, null]], "Eigenvector Centrality": [[8, "eigenvector-centrality"]], "Environment": [[7, "environment"]], "Euler is everywhere!": [[2, "euler-is-everywhere"]], "Euler\u2019s Algorithm": [[2, null]], "Euler\u2019s Algorithm in NetworkX": [[2, "euler-s-algorithm-in-networkx"]], "Euler\u2019s Method": [[2, "euler-s-method"]], "Example": [[1, "example"], [1, "id2"], [6, "example"]], "Example: Directed Graphs": [[1, "example-directed-graphs"]], "Facebook Network Analysis": [[8, null]], "Formal definition": [[5, "formal-definition"]], "Formalization": [[3, "formalization"]], "Format Guidelines": [[7, "format-guidelines"]], "Generalizing Euler\u2019s Solution": [[2, "generalizing-euler-s-solution"]], "Geographical Threshold Graphs": [[9, "geographical-threshold-graphs"]], "Geographical Threshold Graphs (P(d_{ij}), \\theta)": [[9, "geographical-threshold-graphs-p-d-ij-theta"]], "Geometric Generator Models": [[9, null]], "Geometric/Spatial Networks": [[9, "geometric-spatial-networks"]], "Graph Generators": [[10, null]], "Import package": [[2, "import-package"]], "Import packages": [[0, "import-packages"], [1, "import-packages"], [3, "import-packages"], [6, "import-packages"], [8, "import-packages"], [9, "import-packages"], [11, "import-packages"]], "Individual Model Definitions": [[9, "individual-model-definitions"]], "Introduction and Intuition": [[11, "introduction-and-intuition"]], "Isomorphism - How to find if two graphs are similar?": [[5, null]], "Isomorphism Algorithms": [[5, "isomorphism-algorithms"]], "Kahn\u2019s algorithm": [[1, "kahn-s-algorithm"]], "Level Network": [[3, "level-network"]], "Lowest Common Ancestor": [[6, null]], "Maximum flow in NetworkX": [[3, "maximum-flow-in-networkx"]], "Maximum flow problem": [[3, "maximum-flow-problem"]], "Mixing matrix": [[0, "mixing-matrix"]], "Motivation": [[3, "motivation"]], "Network Communities": [[8, "network-communities"]], "NetworkX implementation": [[1, "networkx-implementation"]], "NetworkX\u2019s Implementation of Lowest Common Ancestor Algorithm": [[6, "networkx-s-implementation-of-lowest-common-ancestor-algorithm"]], "Node Assortativity Coefficients and Correlation Measures": [[0, null]], "Numeric Assortativity Coefficient": [[0, "numeric-assortativity-coefficient"]], "Problem Formulation": [[11, "problem-formulation"]], "Random Geometric Graphs": [[9, "random-geometric-graphs"]], "Random Geometric Graphs (R)": [[9, "random-geometric-graphs-r"]], "References": [[0, "references"], [1, "references"], [2, "references"], [3, "references"], [5, "references"], [6, "references"], [8, "references"], [9, "references"], [11, "references"]], "Reformulating the Problem in Abstract Terms": [[2, "reformulating-the-problem-in-abstract-terms"]], "Residual Capacity & Graph": [[3, "residual-capacity-graph"]], "Seven Bridges of K\u00f6nigsberg": [[2, "seven-bridges-of-konigsberg"]], "Soft Random Geometric Graphs": [[9, "soft-random-geometric-graphs"]], "Soft Random Geometric Graphs (R, P(d_{ij}))": [[9, "soft-random-geometric-graphs-r-p-d-ij"]], "Some Tips": [[7, "some-tips"]], "Some classes of graphs with solution in polynomial time": [[5, "some-classes-of-graphs-with-solution-in-polynomial-time"]], "State of the art": [[5, "state-of-the-art"]], "Step 1. 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"titles": ["Node Assortativity Coefficients and Correlation Measures", "Directed Acyclic Graphs & Topological Sort", "Euler\u2019s Algorithm", "Dinitz\u2019s Algorithm and Applications", "Algorithms", "Isomorphism - How to find if two graphs are similar?", "Lowest Common Ancestor", "Contributors Guide", "Facebook Network Analysis", "Geometric Generator Models", "Graph Generators", "Sudoku and Graph Coloring", "Welcome to nx-guides!"], "titleterms": {"": [1, 2, 3, 6], "1": [1, 2, 6, 7], "2": [1, 2, 6, 7], "3": [1, 2, 6, 7], "4": [1, 6, 7], "5": 7, "6": 7, "7": 7, "8": 7, "9": 7, "A": [7, 9], "The": 1, "about": 12, "abstract": 2, "acycl": 1, "add": 7, "addendum": 1, "advanc": 5, "algorithm": [1, 2, 3, 4, 5, 6, 7], "all": [1, 6], "alpha": 9, "analysi": 8, "ancestor": 6, "applic": [1, 3, 5], "ar": [5, 7], "art": 5, "assort": [0, 8], "attribut": [0, 8], "augment": 3, "basic": 8, "between": 8, "bridg": [2, 8], "brief": 7, "capac": 3, "central": 8, "chang": 1, "check": [1, 6], "circuit": 2, 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5, "visual": 8, "wai": 7, "waxman": 9, "welcom": 12, "well": 1, "what": [5, 7], "which": 6, "why": 5, "work": 1, "world": 7, "yet": 7, "you": 7, "your": 7}}) \ No newline at end of file +Search.setIndex({"alltitles": {"1. Eulerian Circuit Implementation": [[2, "eulerian-circuit-implementation"]], "1. Your notebook should be a .md file.": [[7, "your-notebook-should-be-a-md-file"]], "2. Eulerian Path Implementation": [[2, "eulerian-path-implementation"]], "2. Use code-generated images as much as possible.": [[7, "use-code-generated-images-as-much-as-possible"]], "3. Add requirements to requirements.txt": [[7, "add-requirements-to-requirements-txt"]], "3. Examples": [[2, "examples"]], "4. User input is not supported yet.": [[7, "user-input-is-not-supported-yet"]], "5. Do not forget to add path of your notebook to index.md.": [[7, "do-not-forget-to-add-path-of-your-notebook-to-index-md"]], "6. Header Levels": [[7, "header-levels"]], "7. You do not need to implement the algorithm in the same exact way as done inside NetworkX.": [[7, "you-do-not-need-to-implement-the-algorithm-in-the-same-exact-way-as-done-inside-networkx"]], "8. Feel free to use real-world datasets": [[7, "feel-free-to-use-real-world-datasets"]], "9. What if the tests are still failing?": [[7, "what-if-the-tests-are-still-failing"]], "A Brief Roadmap for First-time Contributors": [[7, "a-brief-roadmap-for-first-time-contributors"]], "A Motivating Example": [[9, "a-motivating-example"]], "About": [[12, "about"]], "Addendum: The graph may have changed during the iteration.": [[1, "addendum-the-graph-may-have-changed-during-the-iteration"]], "Addendum: Topological sort works on multigraphs as well.": [[1, "addendum-topological-sort-works-on-multigraphs-as-well"]], "Advanced Algorithms": [[5, "advanced-algorithms"]], "Algorithm": [[3, "algorithm"]], "Algorithms": [[4, null]], "Analysis": [[8, "analysis"]], "Applications": [[1, "applications"], [1, "id3"], [3, "applications"], [5, "applications"]], "Assortativity": [[0, "assortativity"], [8, "assortativity"]], "Assortativity Example": [[0, "assortativity-example"]], "Assortativity coefficients": [[0, "assortativity-coefficients"]], "Attribute Assortativity Coefficient": [[0, "attribute-assortativity-coefficient"]], "Augmenting Path & Flow": [[3, "augmenting-path-flow"]], "Basic topological attributes": [[8, "basic-topological-attributes"]], "Betweenness Centrality": [[8, "betweenness-centrality"]], "Bridges": [[8, "bridges"]], "Centrality measures": [[8, "centrality-measures"]], "Closeness Centrality": [[8, "closeness-centrality"]], "Clustering Effects": [[8, "clustering-effects"]], "Combine all steps.": [[1, "combine-all-steps"]], "Contents": [[12, "contents"]], "Contributors Guide": [[7, null]], "Definition": [[1, "definition"], [1, "id1"], [1, "id5"]], "Definitions": [[6, "definitions"]], "Degree Assortativity Coefficient": [[0, "degree-assortativity-coefficient"]], "Degree Centrality": [[8, "degree-centrality"]], "Dinitz\u2019s Algorithm and Applications": [[3, null]], "Dinitz\u2019s algorithm": [[3, "dinitz-s-algorithm"]], "Directed Acyclic Graph": [[1, "directed-acyclic-graph"]], "Directed Acyclic Graphs & Topological Sort": [[1, null]], "Eigenvector Centrality": [[8, "eigenvector-centrality"]], "Environment": [[7, "environment"]], "Euler is everywhere!": [[2, "euler-is-everywhere"]], "Euler\u2019s Algorithm": [[2, null]], "Euler\u2019s Algorithm in NetworkX": [[2, "euler-s-algorithm-in-networkx"]], "Euler\u2019s Method": [[2, "euler-s-method"]], "Example": [[1, "example"], [1, "id2"], [6, "example"]], "Example: Directed Graphs": [[1, "example-directed-graphs"]], "Facebook Network Analysis": [[8, null]], "Formal definition": [[5, "formal-definition"]], "Formalization": [[3, "formalization"]], "Format Guidelines": [[7, "format-guidelines"]], "Generalizing Euler\u2019s Solution": [[2, "generalizing-euler-s-solution"]], "Geographical Threshold Graphs": [[9, "geographical-threshold-graphs"]], "Geographical Threshold Graphs (P(d_{ij}), \\theta)": [[9, "geographical-threshold-graphs-p-d-ij-theta"]], "Geometric Generator Models": [[9, null]], "Geometric/Spatial Networks": [[9, "geometric-spatial-networks"]], "Graph Generators": [[10, null]], "Import package": [[2, "import-package"]], "Import packages": [[0, "import-packages"], [1, "import-packages"], [3, "import-packages"], [6, "import-packages"], [8, "import-packages"], [9, "import-packages"], [11, "import-packages"]], "Individual Model Definitions": [[9, "individual-model-definitions"]], "Introduction and Intuition": [[11, "introduction-and-intuition"]], "Isomorphism - How to find if two graphs are similar?": [[5, null]], "Isomorphism Algorithms": [[5, "isomorphism-algorithms"]], "Kahn\u2019s algorithm": [[1, "kahn-s-algorithm"]], "Level Network": [[3, "level-network"]], "Lowest Common Ancestor": [[6, null]], "Maximum flow in NetworkX": [[3, "maximum-flow-in-networkx"]], "Maximum flow problem": [[3, "maximum-flow-problem"]], "Mixing matrix": [[0, "mixing-matrix"]], "Motivation": [[3, "motivation"]], "Network Communities": [[8, "network-communities"]], "NetworkX implementation": [[1, "networkx-implementation"]], "NetworkX\u2019s Implementation of Lowest Common Ancestor Algorithm": [[6, "networkx-s-implementation-of-lowest-common-ancestor-algorithm"]], "Node Assortativity Coefficients and Correlation Measures": [[0, null]], "Numeric Assortativity Coefficient": [[0, "numeric-assortativity-coefficient"]], "Problem Formulation": [[11, "problem-formulation"]], "Random Geometric Graphs": [[9, "random-geometric-graphs"]], "Random Geometric Graphs (R)": [[9, "random-geometric-graphs-r"]], "References": [[0, "references"], [1, "references"], [2, "references"], [3, "references"], [5, "references"], [6, "references"], [8, "references"], [9, "references"], [11, "references"]], "Reformulating the Problem in Abstract Terms": [[2, "reformulating-the-problem-in-abstract-terms"]], "Residual Capacity & Graph": [[3, "residual-capacity-graph"]], "Seven Bridges of K\u00f6nigsberg": [[2, "seven-bridges-of-konigsberg"]], "Soft Random Geometric Graphs": [[9, "soft-random-geometric-graphs"]], "Soft Random Geometric Graphs (R, P(d_{ij}))": [[9, "soft-random-geometric-graphs-r-p-d-ij"]], "Some Tips": [[7, "some-tips"]], "Some classes of graphs with solution in polynomial time": [[5, "some-classes-of-graphs-with-solution-in-polynomial-time"]], "State of the art": [[5, "state-of-the-art"]], "Step 1. Initialize in-degrees.": [[1, "step-1-initialize-in-degrees"]], "Step 1: Check if the type of input graph is DAG.": [[6, "step-1-check-if-the-type-of-input-graph-is-dag"]], "Step 2. Initialize first level.": [[1, "step-2-initialize-first-level"]], "Step 2: Find ancestors of all nodes in G.": [[6, "step-2-find-ancestors-of-all-nodes-in-g"]], "Step 3. Move from one level to the next.": [[1, "step-3-move-from-one-level-to-the-next"]], "Step 3: Find common ancestors": [[6, "step-3-find-common-ancestors"]], "Step 4. 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